No description has been provided for this image
Open In Colab

TAREA CALIFICADA - GEODATAFRAMES¶

Exercise 1¶

  1. Follow the same steps in this last section to plot three maps of one country. Do not use Brazil.
  2. Plot your three layers.
In [ ]:
#importing useful libs
import geopandas as gpd
from  fiona import listlayers
import requests
import pandas as pd
In [ ]:
linkWorldMap="https://github.com/CienciaDeDatosEspacial/intro_geodataframe/raw/main/maps/worldMaps.gpkg"
listlayers(linkWorldMap)
Out[ ]:
['countries', 'rivers', 'cities', 'indicators']
In [ ]:
countries=gpd.read_file(linkWorldMap,layer='countries')
In [ ]:
denmark=countries[countries.COUNTRY=='Denmark']
In [ ]:
DenmarkBoundariesUrl="https://github.com/rubbims/Geodataframes/raw/main/maps/kontur_boundaries_DK_20230628.gpkg"
boundaries=gpd.read_file(DenmarkBoundariesUrl)
In [ ]:
DenmarkSeaportsUrl = "https://github.com/rubbims/Geodataframes/raw/main/maps/hotosm_dnk_sea_ports.gpkg"
seaports = gpd.read_file(DenmarkSeaportsUrl)
In [ ]:
DenmarkRailwaysUrl = "https://github.com/rubbims/Geodataframes/raw/main/maps/hotosm_dnk_railways.gpkg"
railways = gpd.read_file(DenmarkRailwaysUrl)
In [ ]:
DenmarkAirportsUrl = "https://github.com/rubbims/Geodataframes/raw/main/maps/hotosm_dnk_airports.gpkg"
airports=gpd.read_file(DenmarkAirportsUrl)
In [ ]:
boundaries_clipped = gpd.clip(gdf=boundaries,
                          mask=denmark)
seaports_clipped = gpd.clip(gdf=seaports,
                          mask=denmark)
railways_clipped = gpd.clip(gdf=railways,
                               mask=denmark)
airports_clipped = gpd.clip(gdf=airports,
                               mask=denmark)
In [ ]:
#map of seaports
base = boundaries.plot(facecolor="red", edgecolor='black', linewidth=0.4,figsize=(15,15))
seaports_clipped.plot(marker='+', color='yellow', markersize=15,
                    ax=base)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#map of railways
base = boundaries.plot(facecolor="red", edgecolor='black', linewidth=0.4,figsize=(15,15))
railways_clipped.plot(marker='+', color='green', markersize=15,
                    ax=base)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#map of airports
base = boundaries.plot(facecolor="red", edgecolor='black', linewidth=0.4,figsize=(15,15))
airports_clipped.plot(marker='*', color='blue', markersize=15,
                    ax=base)
Out[ ]:
<Axes: >
No description has been provided for this image

Exercise 2¶

  1. Reproject your country's map layers.
  2. Plot the reprojected layers
  3. Save the reprojected layers
In [ ]:
denmark.crs.is_projected
Out[ ]:
False
In [ ]:
denmark.centroid
#so, we need to re-project

<ipython-input-213-0eda7ecc094f>:1: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  denmark.centroid
Out[ ]:
55    POINT (10.04630 55.96340)
dtype: geometry
In [ ]:
#we choose 4096 since its widely adpted in Europe
denmark.to_crs(4096).crs.axis_info
Out[ ]:
[Axis(name=Easting, abbrev=E, direction=east, unit_auth_code=EPSG, unit_code=9001, unit_name=metre),
 Axis(name=Northing, abbrev=N, direction=north, unit_auth_code=EPSG, unit_code=9001, unit_name=metre)]
In [ ]:
#true centroid
denmark.to_crs(4096).centroid
Out[ ]:
55    POINT (491579.011 1214846.509)
dtype: geometry
In [ ]:
#as we can see the centroid is on the projected map
base4096=denmark.to_crs(4096).plot(color='red')
denmark.to_crs(4096).centroid.plot(color='yellow',ax=base4096)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#we commit the projected ver of each layer
denmark_4096=denmark.to_crs(4096)
seaports_denmark_4096=seaports_clipped.to_crs(denmark_4096.crs)
railways_denmark_4096=railways_clipped.to_crs(denmark_4096.crs)
airports_denmark_4096=airports_clipped.to_crs(denmark_4096.crs)
In [ ]:
airports_denmark_4096
Out[ ]:
osm_id capacity:persons building source name addr:full aeroway addr:city emergency:helipad operator:type emergency geometry
698 95461033 None hangar None None None hangar None None None None POLYGON ((570896.174 1069316.719, 570909.428 1...
2185 604537715 None hangar None None None hangar None None None None POLYGON ((570850.801 1069324.406, 570864.055 1...
2184 604537714 None hangar None None None hangar None None None None POLYGON ((570834.737 1069327.117, 570847.991 1...
2186 604537716 None hangar None None None hangar None None None None POLYGON ((570781.861 1069336.144, 570797.333 1...
1932 425969033 None hangar None None None hangar None None None None POLYGON ((571191.467 1069330.179, 571209.290 1...
... ... ... ... ... ... ... ... ... ... ... ... ...
1993 486028944 None None None None None taxiway None None None None LINESTRING (514235.131 1386174.411, 514224.318...
1057 119830058 None yes None None None hangar None None None None POLYGON ((514187.039 1386194.191, 514186.980 1...
1056 119830052 None yes None Company Flight K/S None hangar None None None None POLYGON ((514127.373 1386207.119, 514126.993 1...
1994 486028945 None None None None None taxiway None None None None LINESTRING (514235.289 1386197.265, 514223.674...
1976 476614789 None None None None None helipad None None None None POLYGON ((536182.870 1407990.695, 536185.396 1...

1486 rows × 12 columns

In [ ]:
#replotting seaports
base = denmark_4096.plot(facecolor="red", edgecolor='black', linewidth=0.4,figsize=(15,15))
seaports_denmark_4096.plot(marker='*', color='green', markersize=15,
                    ax=base)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#replotting railways
base = denmark_4096.plot(facecolor="red", edgecolor='black', linewidth=0.4,figsize=(15,15))
railways_denmark_4096.plot(marker='.', color='yellow', markersize=15,
                    ax=base)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#replotting airports
base = denmark_4096.plot(facecolor="red", edgecolor='black', linewidth=0.4,figsize=(15,15))
airports_denmark_4096.plot(marker='.', color='blue', markersize=15,
                    ax=base)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#lets save the projected ver of each layer into gpkg
denmark_4096.to_file("denmark4096.gpkg", driver="GPKG")
seaports_denmark_4096.to_file("seaports4096.gpkg", driver="GPKG")
railways_denmark_4096.to_file("railways4096.gpkg", driver="GPKG")
airports_denmark_4096.to_file("airports4096.gpkg", driver="GPKG")

Exercise 3¶

  1. Find the airports for your country here. The data is in a CSV file.
  2. Create projected layer of airports.
  3. Plot all the layers and airports on top.
In [ ]:
import pandas as pd
In [ ]:
#reading denmark airports csv
airports_url = "https://github.com/rubbims/Geodataframes/raw/main/data/dk-airports.csv"
dm_airports = pd.read_csv(airports_url)
# some rows
dm_airports.head()
Out[ ]:
id ident type name latitude_deg longitude_deg elevation_ft continent country_name iso_country region_name iso_region local_region municipality scheduled_service gps_code iata_code local_code home_link wikipedia_link keywords score last_updated
0 2542 EKCH large_airport Copenhagen Kastrup Airport 55.617901 12.656000 17.0 EU Denmark DK Capital Region DK-84 84 Copenhagen 1 EKCH CPH NaN http://www.cph.dk/en/ https://en.wikipedia.org/wiki/Copenhagen_Airport København, Malmö 1020075 2015-09-19T15:12:02+00:00
1 2541 EKBI large_airport Billund Airport 55.740496 9.160452 247.0 EU Denmark DK South Denmark DK-83 83 Billund 1 EKBI BLL NaN http://www.billund-airport.dk/?sc_lang=en https://en.wikipedia.org/wiki/Billund_Airport NaN 1275 2023-12-04T15:48:16+00:00
2 2562 EKYT medium_airport Aalborg Airport 57.094763 9.849930 10.0 EU Denmark DK North Jutland DK-81 81 Aalborg 1 EKYT AAL NaN http://www.aal.dk https://en.wikipedia.org/wiki/Aalborg_Airport NaN 750 2023-12-04T15:44:41+00:00
3 2540 EKAH medium_airport Aarhus Airport 56.303331 10.618286 82.0 EU Denmark DK Central Jutland DK-82 82 Aarhus 1 EKAH AAR NaN http://www.aar.dk/default.asp?id=87 https://en.wikipedia.org/wiki/Aarhus_Airport NaN 750 2021-10-06T16:42:48+00:00
4 2551 EKRK medium_airport Copenhagen Roskilde Airport 55.585602 12.131400 146.0 EU Denmark DK Zealand DK-85 85 Copenhagen 1 EKRK RKE NaN NaN https://en.wikipedia.org/wiki/Roskilde_Airport København 575 2024-04-06T20:51:02+00:00
In [ ]:
#we keep the same columns as the example
keep=['name','type','latitude_deg', 'longitude_deg','elevation_ft','region_name','municipality']
dm_airports=dm_airports.loc[:,keep]
dm_airports.info()
#latitude,longitude and elevation are already numeric

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 138 entries, 0 to 137
Data columns (total 7 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   name           138 non-null    object 
 1   type           138 non-null    object 
 2   latitude_deg   138 non-null    float64
 3   longitude_deg  138 non-null    float64
 4   elevation_ft   59 non-null     float64
 5   region_name    138 non-null    object 
 6   municipality   95 non-null     object 
dtypes: float64(3), object(4)
memory usage: 7.7+ KB
In [ ]:
#Reserved names must not be used, lets rename type
dm_airports.rename(columns={'type':'kind'},inplace=True)
dm_airports.head()
Out[ ]:
name kind latitude_deg longitude_deg elevation_ft region_name municipality
0 Copenhagen Kastrup Airport large_airport 55.617901 12.656000 17.0 Capital Region Copenhagen
1 Billund Airport large_airport 55.740496 9.160452 247.0 South Denmark Billund
2 Aalborg Airport medium_airport 57.094763 9.849930 10.0 North Jutland Aalborg
3 Aarhus Airport medium_airport 56.303331 10.618286 82.0 Central Jutland Aarhus
4 Copenhagen Roskilde Airport medium_airport 55.585602 12.131400 146.0 Zealand Copenhagen
In [ ]:
#lets reproject all layer including new one
#we use 25832 since is commonly used in the north of europe
airportscsv=airports.to_crs(25832)
denmark_25832=denmark_4096.to_crs(25832)
seaports_denmark_25832=seaports_denmark_4096.to_crs(denmark_25832.crs)
railways_denmark_25832=railways_denmark_4096.to_crs(denmark_25832.crs)
airports_denmark_25832=airports_denmark_4096.to_crs(denmark_25832.crs)
In [ ]:
#replotting seaports w airportscsv on top
base = denmark_25832.plot(facecolor="white",
                      edgecolor='black',
                      linewidth=0.1,
                      figsize=(12,12))
seaports_denmark_25832.plot(edgecolor='blue', linewidth=0.4,
            ax=base)
airportscsv.plot(marker='*', color='red', markersize=1,alpha=1,
            ax=base)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#replotting railways w airportscsv on top
base = denmark_25832.plot(facecolor="white",
                      edgecolor='black',
                      linewidth=0.1,
                      figsize=(12,12))
railways_denmark_25832.plot(edgecolor='blue', linewidth=0.4,
            ax=base)
airportscsv.plot(marker='*', color='red', markersize=1,alpha=1,
            ax=base)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#replotting airports w airportscsv on top
base = denmark_25832.plot(facecolor="white",
                      edgecolor='black',
                      linewidth=0.1,
                      figsize=(12,12))
airports_denmark_25832.plot(edgecolor='blue', linewidth=0.4,
            ax=base)
airportscsv.plot(marker='*', color='red', markersize=1,alpha=1,
            ax=base)
#airportscsv has more info than our layer
Out[ ]:
<Axes: >
No description has been provided for this image

Exercise 4¶

  1. Check if your country is a polygon or multipolygon.

  2. Recover just the boundaries of that country.

  3. Turn the boundary into a GDF.

In [ ]:
denmark_25832
#is a multipolygon
Out[ ]:
COUNTRY geometry
55 Denmark MULTIPOLYGON (((464205.349 6281709.969, 465896...
In [ ]:
# Getting the border lines from denmark25832
denmark_25832.boundary.plot()
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
type(denmark_25832.boundary)
#we got geoseries
Out[ ]:
geopandas.geoseries.GeoSeries
def __init__(data=None, index=None, crs=None, **kwargs)
/usr/local/lib/python3.10/dist-packages/geopandas/geoseries.pyA Series object designed to store shapely geometry objects.

Parameters
----------
data : array-like, dict, scalar value
    The geometries to store in the GeoSeries.
index : array-like or Index
    The index for the GeoSeries.
crs : value (optional)
    Coordinate Reference System of the geometry objects. Can be anything accepted by
    :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
    such as an authority string (eg "EPSG:4326") or a WKT string.

kwargs
    Additional arguments passed to the Series constructor,
     e.g. ``name``.

Examples
--------

>>> from shapely.geometry import Point
>>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)])
>>> s
0    POINT (1.00000 1.00000)
1    POINT (2.00000 2.00000)
2    POINT (3.00000 3.00000)
dtype: geometry

>>> s = geopandas.GeoSeries(
...     [Point(1, 1), Point(2, 2), Point(3, 3)], crs="EPSG:3857"
... )
>>> s.crs  # doctest: +SKIP
<Projected CRS: EPSG:3857>
Name: WGS 84 / Pseudo-Mercator
Axis Info [cartesian]:
- X[east]: Easting (metre)
- Y[north]: Northing (metre)
Area of Use:
- name: World - 85°S to 85°N
- bounds: (-180.0, -85.06, 180.0, 85.06)
Coordinate Operation:
- name: Popular Visualisation Pseudo-Mercator
- method: Popular Visualisation Pseudo Mercator
Datum: World Geodetic System 1984
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

>>> s = geopandas.GeoSeries(
...    [Point(1, 1), Point(2, 2), Point(3, 3)], index=["a", "b", "c"], crs=4326
... )
>>> s
a    POINT (1.00000 1.00000)
b    POINT (2.00000 2.00000)
c    POINT (3.00000 3.00000)
dtype: geometry

>>> s.crs
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

See Also
--------
GeoDataFrame
pandas.Series
In [ ]:
#lets transform it to geodf
denmark_25832.boundary.to_frame()
Out[ ]:
0
55 MULTILINESTRING ((464205.349 6281709.969, 4658...
In [ ]:
#lets transform it to geodf
borderdm=denmark_25832.boundary.to_frame()
#name of country
borderdm['name']='Denmark'
#renaming geometry column
borderdm.rename(columns={0:'geometry'},inplace=True)
#setting geometry columns
borderdm = borderdm.set_geometry("geometry")
# verifying:
borderdm
Out[ ]:
geometry name
55 MULTILINESTRING ((464205.349 6281709.969, 4658... Denmark

Exercise 5 (conditional)¶

  1. Look for sub administrative divisions of your country
In [ ]:
# https://data.humdata.org/dataset/whosonfirst-data-admin-dnk

#from google.colab import drive
#drive.mount('/content/drive')

# URLs correctas a los archivos raw en GitHub
denmark_states_url = "https://raw.githubusercontent.com/rubbims/Geodataframes/main/maps/whosonfirst-data-admin-dk-latest/whosonfirst-data-admin-dk-region-polygon.shp"
denmark_municipalities_url = "https://raw.githubusercontent.com/rubbims/Geodataframes/main/maps/whosonfirst-data-admin-dk-latest/whosonfirst-data-admin-dk-localadmin-polygon.shp"

# Lee los archivos directamente desde las URLs
denmark_states = gpd.read_file(denmark_states_url)
denmark_municipalities = gpd.read_file(denmark_municipalities_url)
In [ ]:
from matplotlib import pyplot as plt
fig, (ax1, ax2) = plt.subplots(ncols=2, sharex=False, sharey=False, figsize=(12,12))

denmark_states.cx[0:,:].plot(ax=ax1, facecolor='lightgrey', edgecolor='black')
denmark_municipalities.plot(ax=ax2, facecolor='lightgrey', edgecolor='black',linewidth=0.2)
Out[ ]:
<Axes: >
No description has been provided for this image
  1. Check all the CRSs of those divisions
In [ ]:
print(type(denmark_states), type(denmark_municipalities)) #vemos que es un gdf
print(denmark_states.geometry.head()) # es multipoligonal
print(denmark_states.crs, denmark_municipalities.crs) # sí tiene crs

<class 'geopandas.geodataframe.GeoDataFrame'> <class 'geopandas.geodataframe.GeoDataFrame'>
0    MULTIPOLYGON (((-6.91145 61.62627, -6.89056 61...
1    MULTIPOLYGON (((8.40776 55.45601, 8.40627 55.4...
2    MULTIPOLYGON (((14.70088 55.14588, 14.69875 55...
3    MULTIPOLYGON (((10.98130 54.83016, 10.97817 54...
4    MULTIPOLYGON (((11.02050 57.24190, 11.06478 57...
Name: geometry, dtype: geometry
EPSG:4326 EPSG:4326
In [ ]:
print(denmark_states.crs.axis_info, '\n', denmark_municipalities.crs.axis_info) # ESTÁ EN POLARES, DEBEMOS TRANSFORMAR LUEGO A METROS
print(denmark_states.crs, denmark_municipalities.crs) # sí tiene crs

[Axis(name=Geodetic latitude, abbrev=Lat, direction=north, unit_auth_code=EPSG, unit_code=9122, unit_name=degree), Axis(name=Geodetic longitude, abbrev=Lon, direction=east, unit_auth_code=EPSG, unit_code=9122, unit_name=degree)] 
 [Axis(name=Geodetic latitude, abbrev=Lat, direction=north, unit_auth_code=EPSG, unit_code=9122, unit_name=degree), Axis(name=Geodetic longitude, abbrev=Lon, direction=east, unit_auth_code=EPSG, unit_code=9122, unit_name=degree)]
EPSG:4326 EPSG:4326
In [ ]:
denmark = countries[countries.COUNTRY=='Dinamarca']
denmark.crs #4326
Out[ ]:
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
  1. If you find one CRS is missing, fill the CRS with the right projection.
In [ ]:
# ya tienen su CRS, lo que nos falta es hacer la correcta proyección a un crs favorable

# tomamos "FEH2010 / Fehmarnbelt TM" con "EPSG:5596"

denmark_states = denmark_states.to_crs(5596)
denmark_municipalities = denmark_municipalities.to_crs(5596)

Exercise 6¶

1. Create some subset of polygons with your country data.
In [ ]:
denmark_municipalities.plot(ax=ax2, facecolor='lightgrey', edgecolor='black',linewidth=0.2)
Out[ ]:
<Axes: >
<Figure size 640x480 with 0 Axes>
In [ ]:
pd.set_option('display.max_columns', None)
denmark_municipalities.head(10)
Out[ ]:
id parent_id name placetype placelocal country repo lat lon min_lat min_lon max_lat max_lon min_zoom max_zoom min_label max_label modified is_funky population country_id region_id county_id concord_ke concord_id iso_code hasc_id gn_id wd_id name_ara name_ben name_deu name_eng name_ell name_fas name_fra name_heb name_hin name_hun name_ind name_ita name_jpn name_kor name_nld name_pol name_por name_rus name_spa name_swe name_tur name_ukr name_urd name_vie name_zho geom_src geometry
0 1159297717 85682589 Holbaek localadmin kommune DK whosonfirst-data-admin-dk 55.675774 11.573848 55.508929 11.350962 55.809169 11.858369 12 None 13.5 18 2023-09-28 None 71541 85633121 85682589 None dk-geodk:code 316 None None None None None None None Holbaek None None None None None None None None None None None None None None None None None None None None None dk-geodk MULTIPOLYGON (((1028605.296 6171633.116, 10286...
1 1159297719 85682589 Stevns localadmin kommune DK whosonfirst-data-admin-dk 55.326944 12.334101 55.235697 12.107425 55.427477 12.456528 12 None 13.5 18 2023-09-28 None 22805 85633121 85682589 None dk-geodk:code 336 None None None None None None None Stevns None None None None None None None None None None None None None None None None None None None None None dk-geodk MULTIPOLYGON (((1071296.650 6133778.538, 10712...
2 1159297721 85682597 Skanderborg localadmin kommune DK whosonfirst-data-admin-dk 56.076149 9.872121 55.953987 9.633145 56.216936 10.092933 12 None 13.5 18 2023-09-28 None 62678 85633121 85682597 None dk-geodk:code 746 None None None None None None Skanderborg Skanderborg None None Skanderborg None None None None Skanderborg None None Skanderborg Skanderborg Skanderborg Сканнерборг Skanderborg Skanderborg None None None None 斯坎訥堡自治市 dk-geodk POLYGON ((894560.919 6217046.948, 894711.815 6...
3 1159297723 85682581 Lyngby-Taarbaek localadmin kommune DK whosonfirst-data-admin-dk 55.781735 12.510815 55.759558 12.413286 55.809710 12.596912 12 None 13.5 18 2023-09-28 None 56214 85633121 85682581 None dk-geodk:code 173 None None None None None None None Lyngby-Taarbaek None None None None None None None None None None None None None None None None None None None None None dk-geodk POLYGON ((1078317.333 6187878.034, 1078327.597...
4 1159297725 85682597 Herning localadmin kommune DK whosonfirst-data-admin-dk 56.203869 8.933449 55.879568 8.433759 56.414040 9.187847 12 None 13.5 18 2023-09-28 None 89127 85633121 85682597 None dk-geodk:code 657 None None None None هرنينغ None Herning Herning None هرنینگ Herning None None None Munisipalitas Herning Herning ヘアニング 헤르닝 Herning Herning None Хернинг Herning Herning Herning Гернінг None Herning 海宁 dk-geodk MULTIPOLYGON (((832268.655 6226344.658, 832274...
5 1159297727 85682581 Copenhagen localadmin kommune DK whosonfirst-data-admin-dk 55.698354 12.571489 55.612853 12.452998 55.732711 12.734255 12 None 13.5 18 2023-09-28 None 632340 85633121 85682581 None dk-geodk:code 101 None None None None كوبنهاغن কোপেনহেগেন Kopenhagen Copenhagen Κοπεγχάγη کپنهاگ Copenhague קופנהגן कोपनहेगन Koppenhága Kopenhagen Copenaghen コペンハーゲン 코펜하겐 Kopenhagen Kopenhaga Copenhaga Копенгаген Copenhague Köpenhamn Kopenhag Копенгаген کوپن ہیگن Copenhagen 哥本哈根 dk-geodk MULTIPOLYGON (((1088066.280 6176427.948, 10880...
6 1159297729 85682581 Frederikssund localadmin kommune DK whosonfirst-data-admin-dk 55.793708 11.985618 55.714237 11.845962 55.937969 12.231291 12 None 13.5 18 2023-09-28 None 45223 85633121 85682581 None dk-geodk:code 250 None None None None None None Frederikssund Frederikssund None None None None None Frederikssund None None None None Frederikssund Frederikssund None Фредерикссунн Frederikssund Frederikssund None None None None None dk-geodk MULTIPOLYGON (((1043096.801 6177341.505, 10430...
7 1159297733 85682581 Albertslund localadmin kommune DK whosonfirst-data-admin-dk 55.679988 12.347031 55.641959 12.311000 55.709789 12.396280 12 None 13.5 18 2023-09-28 None 27731 85633121 85682581 None dk-geodk:code 165 None None None None None None None Albertslund None None None None None None None None None None None Albertslund None None Albertslund Albertslund None None None None None dk-geodk POLYGON ((1065542.084 6171219.759, 1065519.958...
8 1159297735 85682597 Samso localadmin kommune DK whosonfirst-data-admin-dk 55.818602 10.581390 55.764339 10.520158 56.002364 10.793492 12 None 13.5 18 2023-09-28 None 3657 85633121 85682597 None dk-geodk:code 741 None None None None None None None Samso None None None None None None None None None None None None None None None None None None None None None dk-geodk MULTIPOLYGON (((956826.597 6195325.368, 956823...
9 1159297737 85682589 Guldborgsund localadmin kommune DK whosonfirst-data-admin-dk 54.814822 11.950360 54.559078 11.530032 54.971843 12.165719 12 None 13.5 18 2023-09-28 None 60722 85633121 85682589 None dk-geodk:code 376 None None None None None None Guldborgsund Guldborgsund None None Guldborgsund None None None None None グルドボースンド海峡 None Guldborg Sund None None None Guldborgsund Guldborg Sund None None None None 古爾德堡海峽 dk-geodk MULTIPOLYGON (((1027998.825 6079346.001, 10280...
In [ ]:
denmark_municipalities['parent_id'].value_counts() # a qué región pertenece cada localidad
Out[ ]:
parent_id
 85682581    29
 85682575    22
 85682597    19
 85682589    17
 85682593    11
-1            1
Name: count, dtype: int64
In [ ]:
denmark_municipalities[denmark_municipalities['parent_id']==85682581].plot()
Out[ ]:
<Axes: >
No description has been provided for this image
  1. Use Unary UNION with those polygons.
In [ ]:
parent_id_union=denmark_municipalities[denmark_municipalities['parent_id']==85682581].unary_union
parent_id_union
Out[ ]:
No description has been provided for this image
  1. Create a geoDF with the result.
In [ ]:
parentGDF = gpd.GeoDataFrame(index=[0],data={'parent_id':85682581},
                 crs=denmark_municipalities.crs,
                 geometry=[parent_id_union])
parentGDF
Out[ ]:
parent_id geometry
0 85682581 MULTIPOLYGON (((1042974.632 6177385.131, 10429...
  1. Use dissolve with the same polygons, and create a geoDF.
In [ ]:
denmark_municipalities.dissolve(by='parent_id').plot(facecolor='yellow', edgecolor='black',linewidth=0.2)

denmark_parentid_diss=denmark_municipalities.dissolve(by='parent_id')
denmark_parentid_diss #este es nuestro geo dataframe
Out[ ]:
geometry id name placetype placelocal country repo lat lon min_lat min_lon max_lat max_lon min_zoom max_zoom min_label max_label modified is_funky population country_id region_id county_id concord_ke concord_id iso_code hasc_id gn_id wd_id name_ara name_ben name_deu name_eng name_ell name_fas name_fra name_heb name_hin name_hun name_ind name_ita name_jpn name_kor name_nld name_pol name_por name_rus name_spa name_swe name_tur name_ukr name_urd name_vie name_zho geom_src
parent_id
-1 MULTIPOLYGON (((1244246.738 6139688.235, 12442... 1394014155 Christianso localadmin kommune DK whosonfirst-data-admin-dk 55.320402 15.188798 55.317257 15.173827 55.330772 15.197400 12 None 13.5 18 2023-09-28 None 84 -1 -1 None dk-geodk:code 411 None None None None None None None Christianso None None None None None None None None None None None None None None None None None None None None None dk-geodk
85682575 MULTIPOLYGON (((882439.770 6083032.984, 882441... 1159297747 Haderslev localadmin kommune DK whosonfirst-data-admin-dk 55.232202 9.374950 55.125549 8.884642 55.382343 9.778885 12 None 13.5 18 2023-09-28 None 55670 85633121 85682575 None dk-geodk:code 510 None None None None أودنسه সভেন্ডবোর্গ Haderslev Haderslev Λάνγκελαντ ادنسه Kerteminde אודנסה ओडिन्से Vejen Langeland Haderslev ランゲラン島 하데르슬레우 Haderslev Haderslev Haderslev Хадерслев Haderslev Haderslev Haderslev Гадерслев اودنسے Langeland 凯特明讷 dk-geodk
85682581 MULTIPOLYGON (((1042974.632 6177385.131, 10429... 1159297723 Lyngby-Taarbaek localadmin kommune DK whosonfirst-data-admin-dk 55.781735 12.510815 55.759558 12.413286 55.809710 12.596912 12 None 13.5 18 2023-09-28 None 56214 85633121 85682581 None dk-geodk:code 173 None None None None كوبنهاغن কোপেনহেগেন Kopenhagen Lyngby-Taarbaek Κοπεγχάγη کپنهاگ Copenhague קופנהגן कोपनहेगन Koppenhága Kopenhagen Copenaghen コペンハーゲン 코펜하겐 Kopenhagen Kopenhaga Copenhaga Копенгаген Copenhague Köpenhamn Kopenhag Копенгаген کوپن ہیگن Copenhagen 哥本哈根 dk-geodk
85682589 MULTIPOLYGON (((976998.632 6074588.187, 977002... 1159297717 Holbaek localadmin kommune DK whosonfirst-data-admin-dk 55.675774 11.573848 55.508929 11.350962 55.809169 11.858369 12 None 13.5 18 2023-09-28 None 71541 85633121 85682589 None dk-geodk:code 316 None None None None لولاند রসক্লাইড Guldborgsund Holbaek Λόλλαντ لولند Guldborgsund ליירה रोसकिल्ड Slagelse Lolland Slagelse グルドボースンド海峡 슬라겔세 Guldborg Sund Slagelse Slagelse Слагельсе Guldborgsund Guldborg Sund Roskilde Слагельсе راسکیلے Slagelse 古爾德堡海峽 dk-geodk
85682593 MULTIPOLYGON (((834510.776 6287190.389, 834508... 1159297741 Vesthimmerland localadmin kommune DK whosonfirst-data-admin-dk 56.813064 9.364557 56.636899 9.068354 57.030598 9.659873 12 None 13.5 18 2023-09-28 None 36727 85633121 85682593 None dk-geodk:code 820 None None None None آلبورغ ফ্রেডেরিকশাভন Aalborg Vesthimmerland Άλμποργκ آلبورگ Aalborg אולבורג फ़्रेडेरिकशॉन Aalborg Aalborg Aalborg オールボー 올보르 Aalborg Aalborg Aalborg Ольборг Aalborg Ålborg Aalborg Ольборг آلبو Aalborg 奥尔堡 dk-geodk
85682597 MULTIPOLYGON (((802338.295 6194475.607, 802338... 1159297721 Skanderborg localadmin kommune DK whosonfirst-data-admin-dk 56.076149 9.872121 55.953987 9.633145 56.216936 10.092933 12 None 13.5 18 2023-09-28 None 62678 85633121 85682597 None dk-geodk:code 746 None None None None هرنينغ ভিবর্গ Skanderborg Skanderborg Ράντερς هرنینگ Skanderborg אורהוס वाइबोर्ग Randers Munisipalitas Herning Skanderborg ヘアニング 헤르닝 Skanderborg Skanderborg Skanderborg Сканнерборг Skanderborg Skanderborg Herning Гернінг آرہس Herning 斯坎訥堡自治市 dk-geodk
No description has been provided for this image

Exercise 7¶

  1. Select some points from your maps.
In [ ]:
#denmark = countries[countries.COUNTRY=='Dinamarca']
#denmark_5596 = denmark.to_crs(5596)
denmark_4096.crs.axis_info
Out[ ]:
[Axis(name=Easting, abbrev=E, direction=east, unit_auth_code=EPSG, unit_code=9001, unit_name=metre),
 Axis(name=Northing, abbrev=N, direction=north, unit_auth_code=EPSG, unit_code=9001, unit_name=metre)]
In [ ]:
denmark_4096.centroid.x.values[0]
Out[ ]:
491579.0106276414
In [ ]:
dm_airports
Out[ ]:
name kind latitude_deg longitude_deg elevation_ft region_name municipality
0 Copenhagen Kastrup Airport large_airport 55.617901 12.656000 17.0 Capital Region Copenhagen
1 Billund Airport large_airport 55.740496 9.160452 247.0 South Denmark Billund
2 Aalborg Airport medium_airport 57.094763 9.849930 10.0 North Jutland Aalborg
3 Aarhus Airport medium_airport 56.303331 10.618286 82.0 Central Jutland Aarhus
4 Copenhagen Roskilde Airport medium_airport 55.585602 12.131400 146.0 Zealand Copenhagen
... ... ... ... ... ... ... ...
133 Aarhus University Hospital Helipad B heliport 56.190780 10.174920 NaN Central Jutland Aarhus
134 Rigshospitalet Helipad heliport 55.696662 12.566769 NaN Capital Region Copenhagen
135 Holsted Helipad heliport 55.496667 8.910833 NaN South Denmark Holsted
136 Korsør Helipad heliport 55.354250 11.124300 9.0 Zealand Korsør
137 Bodilsker Airstrip closed 55.063671 15.053152 NaN Capital Region Nexø

138 rows × 7 columns

  1. Create the convex hull for those points.
In [ ]:
from shapely.geometry import Point
In [ ]:
# Crear la columna de geometría a partir de latitud y longitud
geometry = [Point(xy) for xy in zip(dm_airports['longitude_deg'], dm_airports['latitude_deg'])]

# Crear un GeoDataFrame
aeropuertos = gpd.GeoDataFrame(dm_airports, geometry=geometry)

# Establecer el sistema de coordenadas (CRS) a WGS84 (EPSG:4326)
aeropuertos.set_crs(epsg=4326, inplace=True)
aeropuertos
Out[ ]:
name kind latitude_deg longitude_deg elevation_ft region_name municipality geometry
0 Copenhagen Kastrup Airport large_airport 55.617901 12.656000 17.0 Capital Region Copenhagen POINT (12.65600 55.61790)
1 Billund Airport large_airport 55.740496 9.160452 247.0 South Denmark Billund POINT (9.16045 55.74050)
2 Aalborg Airport medium_airport 57.094763 9.849930 10.0 North Jutland Aalborg POINT (9.84993 57.09476)
3 Aarhus Airport medium_airport 56.303331 10.618286 82.0 Central Jutland Aarhus POINT (10.61829 56.30333)
4 Copenhagen Roskilde Airport medium_airport 55.585602 12.131400 146.0 Zealand Copenhagen POINT (12.13140 55.58560)
... ... ... ... ... ... ... ... ...
133 Aarhus University Hospital Helipad B heliport 56.190780 10.174920 NaN Central Jutland Aarhus POINT (10.17492 56.19078)
134 Rigshospitalet Helipad heliport 55.696662 12.566769 NaN Capital Region Copenhagen POINT (12.56677 55.69666)
135 Holsted Helipad heliport 55.496667 8.910833 NaN South Denmark Holsted POINT (8.91083 55.49667)
136 Korsør Helipad heliport 55.354250 11.124300 9.0 Zealand Korsør POINT (11.12430 55.35425)
137 Bodilsker Airstrip closed 55.063671 15.053152 NaN Capital Region Nexø POINT (15.05315 55.06367)

138 rows × 8 columns

In [ ]:
# coordinates
centroidX,centroidY = denmark_4096.centroid.x.values[0], denmark_4096.centroid.y.values[0]

# subsets of medium airports
denmark_AirTopLeft=aeropuertos[aeropuertos.kind=='medium_airport'].cx[:centroidX,centroidY:]
denmark_AirTopRight=aeropuertos[aeropuertos.kind=='medium_airport'].cx[centroidX:,centroidY:]
denmark_AirBottomLeft=aeropuertos[aeropuertos.kind=='medium_airport'].cx[:centroidX,:centroidY]
denmark_AirBottomRight=aeropuertos[aeropuertos.kind=='medium_airport'].cx[centroidX:,:centroidY]
In [ ]:
base = denmark_AirTopLeft.plot(facecolor='grey', alpha=0.4)
#denmark_AirTopRight.plot(ax=base,facecolor='orange', alpha=0.4)
denmark_AirBottomLeft.plot(ax=base,facecolor='green', alpha=0.4)
denmark_AirBottomRight.plot(ax=base,facecolor='red', alpha=0.4)
Out[ ]:
<Axes: >
No description has been provided for this image
  1. Turn the hull into a GDF.
In [ ]:
large_airport =  aeropuertos[aeropuertos.kind=='large_airport']
LargeAirport_hull= gpd.GeoDataFrame(index=[0],
                                    crs=large_airport.crs,
                                    geometry=[large_airport.unary_union.convex_hull])
LargeAirport_hull['name']='large airports hull' # optional

# then

LargeAirport_hull
Out[ ]:
geometry name
0 LINESTRING (9.16045 55.74050, 12.65600 55.61790) large airports hull
  1. Plot the hull on top of the country.
In [ ]:
base = boundaries.plot(facecolor="red", edgecolor='black', linewidth=0.5,figsize=(15,15), markersize=0.01)
LargeAirport_hull.plot(ax=base, facecolor='yellow', alpha=0.4)
#denmark_municipalities.dissolve(by='parent_id').plot(facecolor='yellow', edgecolor='black',linewidth=0.2)
Out[ ]:
<Axes: >
No description has been provided for this image